Implementing Analytics in Internal Audit...Implementing Analytics in Internal Audit Jordan Lloyd –...
Transcript of Implementing Analytics in Internal Audit...Implementing Analytics in Internal Audit Jordan Lloyd –...
Implementing Analytics in Internal AuditJordan Lloyd – Senior ManagerRavindra Singh – Manager
What does Success Look Like
To deliver successful analytical insight as an everyday part of the audit process means IA must focus more broadly than on data and technology. The goal is to develop cost effective solutions that are targeted and underpin the audit process to achieve a more efficient and effective audit delivery model.
Becoming analytics-enabled relies on the fundamental building blocks of People, Process, Data and Technology being informed by an Analytics Strategy. This enables the embedding of analytics into the audit lifecycle, the focusing on the right risks at the right time, while aligning analytics to the IA strategy and value drivers of the business.
Benefits of Analytics
Make innovation a centerpiece
Perform more
targeted audits
Perform the same audit
cheaper
Perform the same audit
faster
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Maturity of internal audit analytics
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Planning the journey
Strategy
People
Process
Technology
Data
Analytics vision for audit services
Identifies key projects/programs, stakeholders and metrics
Aligned with corporate and information technology strategies
Organizational structure and program sponsor/manager
Capability / competency acquisition or development
Collaboration and communication planning
Operating model for developing and consuming insights
Analytics prioritization and project management
Methodologies and approaches for analytics
Solution requirements, evaluation, selection and set-up
Technology vendor / license management
Technology architecture and solution optimization
Data acquisition and enrichment
Data model architecture
Data governance and security
Building Blocks
Building a sustainable analytics function requires a foundation of the fundamental building blocks of People Process, Data and Technology, informed by an Analytics Strategy.
Who is the accountable IA owner? What organisational structure do we need to put in place to support our analytical strategy? Who do we need to engage in other departments and what are their roles? What other talents do we need and what is the plan for getting them?
How do we identify the right projects on which to focus our efforts? What are the steps we need to take to ensure that these projects are a success? How will we comply with relevant regulations? What are the risks and how do we mitigate them? How will we measure our progress and test the validity of the insight?
What data do we need to answer the important questions? From where is it sourced – internal, external, licensed, open? How do we bring it together and what are the challenges in transforming, linking and publishing it? What about quality and accuracy?
What tools do we need to process the data? How do we scale up the technology when we need to roll out to the rest of the function? Do we use a self-service or bespoke solution model?
Key Questions to Consider
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Operating Models
= Analytics capability
Organizing for Success Interaction Models
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Multi-dimensional team
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Multi-Disciplinary Approach
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The data analytics team supports the overall IA mission through the successful deployment of team resources at the engagement level. For engagements that require analytic support, there are numerous instances where data specialists and analysts can influence processes to yield successful audits. The following graphic demonstrates the high-level timeline and key interactions for audit analytics.
Audit Interaction Model
CRISP – DM: The Model Development Framework
Movements
Requisitions
Materials Master
Purchase Orders
What is the operating culture? Where have controls failed in the past?
What are the key business processes in the operation? What are the vulnerabilities?
When is data captured in the system?
Where is data stored and in what format?
How can data be merged and cleansed in order to establish records to analyze?
What analytic techniques can be used to identify known high risk scenarios in the data? Are there other scenarios that look similar?
What analytic techniques can be used to identify potential unknown scenarios of control failures in the data?
Business Understanding
Data Understanding
Data Preparation Evaluation Deployment
Which analytic techniques implemented are most reliable in identifying potential risks in the data?
How can analytic techniques be leveraged to identify potential risks on an ongoing basis? What does the end-state solution look like?
1 2 3 5 6
FAMILIARIZE ANALYZE OPERATIONALIZE
Analytics Process
Modeling4
AnalyticsTools /Solutions
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Fals
e P
osit
ive
Rat
e
Unusual Transactions Identified per period
Random Sampling
General Rules
Tailored Rules
Risk Aggregation
Machine Learning
Profile Risk Aggregation
Model maturity development
99.99%
99.5%
99.25%
60%
30%
25%
0.01 400.5 0.75 60 75 100
A noticeable ROI from advanced analytic techniques is the reduction in cost, time and effort, to review transactions and assess if they are truly of concern
Reducing false positives with advanced Analytics
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